{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "nba = pd.read_csv(\"nba_2013.csv\")\n",
    "nba.head(3)\n",
    "\n",
    "point_guards = nba[nba['pos'] == 'PG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\python27\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pts</th>\n",
       "      <th>g</th>\n",
       "      <th>ppg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>930</td>\n",
       "      <td>71</td>\n",
       "      <td>13.098592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>150</td>\n",
       "      <td>20</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>660</td>\n",
       "      <td>79</td>\n",
       "      <td>8.354430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>666</td>\n",
       "      <td>72</td>\n",
       "      <td>9.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>378</td>\n",
       "      <td>55</td>\n",
       "      <td>6.872727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pts   g        ppg\n",
       "24  930  71  13.098592\n",
       "29  150  20   7.500000\n",
       "30  660  79   8.354430\n",
       "38  666  72   9.250000\n",
       "50  378  55   6.872727"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "point_guards['ppg'] = point_guards['pts'] / point_guards['g']\n",
    "\n",
    "# Sanity check, make sure ppg = pts/g\n",
    "point_guards[['pts', 'g', 'ppg']].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "point_guards = point_guards[point_guards['tov'] != 0]\n",
    "point_guards['atr'] = point_guards['ast'] / point_guards['tov']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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46wngDnf/awHnnAzcCGxj+ziToQQ1SrYBO3tW0Gb2O6DD3b+ese9sYK67v6Of96kFOjo6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\nyxvgwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x59fa950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(point_guards['ppg'], point_guards['atr'], c='y')\n",
    "plt.title(\"Point Guards\")\n",
    "plt.xlabel('Points Per Game', fontsize=13)\n",
    "plt.ylabel('Assist Turnover Ratio', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_clusters = 5\n",
    "# Use numpy's random function to generate a list, length: num_clusters, of indices\n",
    "random_initial_points = np.random.choice(point_guards.index, size=num_clusters)\n",
    "# Use the random indices to create the centroids\n",
    "centroids = point_guards.loc[random_initial_points]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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da4d6LSIiIhJF1IJmIiIiIgWLlIiY2XNmNmjBMjM728yeix6WiIiIVIOoLSIHEtT2GMxI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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6584690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(point_guards['ppg'], point_guards['atr'], c='yellow')\n",
    "plt.scatter(centroids['ppg'], centroids['atr'], c='red')\n",
    "plt.title(\"Centroids\")\n",
    "plt.xlabel('Points Per Game', fontsize=13)\n",
    "plt.ylabel('Assist Turnover Ratio', fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def centroids_to_dict(centroids):\n",
    "    dictionary = dict()\n",
    "    # iterating counter we use to generate a cluster_id\n",
    "    counter = 0\n",
    "\n",
    "    # iterate a pandas data frame row-wise using .iterrows()\n",
    "    for index, row in centroids.iterrows():\n",
    "        coordinates = [row['ppg'], row['atr']]\n",
    "        dictionary[counter] = coordinates\n",
    "        counter += 1\n",
    "\n",
    "    return dictionary\n",
    "\n",
    "centroids_dict = centroids_to_dict(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2360679775\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "def calculate_distance(centroid, player_values):\n",
    "    root_distance = 0\n",
    "    \n",
    "    for x in range(0, len(centroid)):\n",
    "        difference = centroid[x] - player_values[x]\n",
    "        squared_difference = difference**2\n",
    "        root_distance += squared_difference\n",
    "\n",
    "    euclid_distance = math.sqrt(root_distance)\n",
    "    return euclid_distance\n",
    "\n",
    "q = [5, 2]\n",
    "p = [3,1]\n",
    "\n",
    "# Sqrt(5) = ~2.24\n",
    "print(calculate_distance(q, p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add the function, `assign_to_cluster`\n",
    "# This creates the column, `cluster`, by applying assign_to_cluster row-by-row\n",
    "# Uncomment when ready\n",
    "\n",
    "# point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)\n",
    "def assign_to_cluster(row):\n",
    "    lowest_distance = -1\n",
    "    closest_cluster = -1\n",
    "    \n",
    "    for cluster_id, centroid in centroids_dict.items():\n",
    "        df_row = [row['ppg'], row['atr']]\n",
    "        euclidean_distance = calculate_distance(centroid, df_row)\n",
    "        \n",
    "        if lowest_distance == -1:\n",
    "            lowest_distance = euclidean_distance\n",
    "            closest_cluster = cluster_id \n",
    "        elif euclidean_distance < lowest_distance:\n",
    "            lowest_distance = euclidean_distance\n",
    "            closest_cluster = cluster_id\n",
    "    return closest_cluster\n",
    "\n",
    "point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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u8Ct3f/8kz78LeMrd3znOOfVAX19fH/X19cUIU0REpCb09/fT0NAARUhEJj00U6ykYiJm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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x676f810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing clusters\n",
    "def visualize_clusters(df, num_clusters):\n",
    "    colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']\n",
    "\n",
    "    for n in range(num_clusters):\n",
    "        clustered_df = df[df['cluster'] == n]\n",
    "        plt.scatter(clustered_df['ppg'], clustered_df['atr'], c=colors[n-1])\n",
    "        plt.xlabel('Points Per Game', fontsize=13)\n",
    "        plt.ylabel('Assist Turnover Ratio', fontsize=13)\n",
    "    plt.show()\n",
    "\n",
    "visualize_clusters(point_guards, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def recalculate_centroids(df):\n",
    "    new_centroids_dict = dict()\n",
    "    # 0..1...2...3...4\n",
    "    for cluster_id in range(0, num_clusters):\n",
    "        # Finish the logic\n",
    "        return new_centroids_dict\n",
    "\n",
    "centroids_dict = recalculate_centroids(point_guards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "centroids_dict = recalculate_centroids(point_guards)\n",
    "point_guards['cluster'] = point_guards.apply(lambda row: assign_to_cluster(row), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fPsPjbwYecffzpzimERgYGBigsbGxFGGKiIjUhcHBQVauXAklSERm3DVTqqRiOmb2MeAb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\nDLE47wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x88dc550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=num_clusters)\n",
    "kmeans.fit(point_guards[['ppg', 'atr']])\n",
    "point_guards['cluster'] = kmeans.labels_\n",
    "\n",
    "visualize_clusters(point_guards, num_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
